Generation of high resolution population density data sets through exploitation of high resolution overhead imagery data and low resolution population density data sets

ABSTRACT

Utilities (e.g., systems, methods, etc.) for automatically generating high resolution population density estimation data sets through manipulation of low resolution population density estimation data sets with high resolution overhead imagery data (e.g., such as overhead imagery data acquired by satellites, aircrafts, etc. of celestial bodies). Stated differently, the present utilities make use of high resolution overhead imagery data to determine how to distribute the population density of a large, low resolution cell (e.g., 1000m) among a plurality of smaller, high resolution cells (e.g., 100m) within the larger cell.

RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.14/063,309, filed Oct. 25, 2013, and entitled “GENERATION OF HIGHRESOLUTION POPULATION DENSITY DATA SETS THROUGH EXPLOITATION OF HIGHRESOLUTION OVERHEAD IMAGERY DATA AND LOW RESOLUTION POPULATION DENSITYDATA SETS,” which claims priority to U.S. Provisional Patent ApplicationNo. 61/871,691, filed Aug. 29, 2013, and entitled “GENERATION OF HIGHRESOLUTION POPULATION DENSITY DATA SETS THROUGH EXPLOITATION OF HIGHRESOLUTION OVERHEAD IMAGERY DATA AND LOW RESOLUTION POPULATION DENSITYDATA SETS,” the entire contents of which are incorporated herein byreference in their entireties as if set forth in full.

BACKGROUND

Information on human settlements is crucial for a wide range ofapplications including emergency response, disaster risk reduction,population estimation/analysis, and urban/regional planning.Urbanization pressure generates environmental impacts, indicatespopulation growth, and relates to risk and disaster vulnerability. Forinstance, the global population passed the mark of 7.0 billion in 2011with more than half of the population living in urban areas. Between2011 and 2050, the urban population is expected to increase by about 2.7billion, passing from 3.6 billion in 2011 to 6.3 billion in 2050. Thepopulation growth in urban areas is projected to be concentrated in thecities and towns of the less developed countries and continents. Asia,in particular, is projected to see its urban population increase by 1.4billion, Africa by 0.9 billion, and Latin America and the Caribbean by0.2 billion.

Population growth is therefore becoming largely an urban phenomenonconcentrated in the developing world resulting in major challenges tomanage the urban development in a sustainable manner. One issue in thisrespect is the availability of up-to-date information on the extent andquality of the urban settlement (e.g., the urban “built-up” or“build-up,” such as man-made 3-dimensional structures) which is largelyunavailable in developing countries. For instance, cities are oftengrowing at a pace that cannot be fully controlled by the local orregional mapping agencies. As demographic pressure increasesexponentially at a global level, the ability to monitor, quantify andcharacterize urbanization processes around the world is becomingparamount. The information about the quality of urban development canprovide precious input for understanding the vulnerability of thepopulation living on our planet.

While overhead imagery could provide information about the world-widebuilt-up environment, there are few global data sets available thatcould be used to map the human settlements. Examples include thenight-time lights of the world based on the Defense MeteorologicalSatellite Program—Operational Linescan System (DMSP-OLS) sensor,Moderate Resolution Imaging Spectroradiometer (MODIS) based landuse/land cover classifications, and global population data sets likeLandScan™ or the gridded population of the world (GPW). In the case ofLandScan and GPW, for instance, available global population data setsinclude population density estimation cell sets at coarse resolutions of0.00833 (approximately 1000 m) or the like (e.g., where each populationdensity estimation cell is in the form of a square that represents 1000m on a side and indicates a particular population density or populationdensity range over a geographic region, city etc.). While suchpopulation density data sets may be somewhat useful in conveying generalpopulation density estimates over large areas, they are limited in theirability to provide more fine grained population density estimates. Inthe case of an urban area, for instance, population densities may varywidely just between adjacent street blocks.

SUMMARY

The inventor has determined that it may be useful to utilize highresolution overhead image data to obtain population density estimationdata sets at resolutions higher than those currently available. In thisregard, disclosed herein are utilities (e.g., systems, methods, etc.)for automatically generating high resolution population densityestimation data sets through manipulation of low resolution populationdensity estimation data sets with high resolution overhead imagery data(e.g., such as overhead imagery data acquired by satellites, aircrafts,etc. of celestial bodies). Stated differently, the present utilitiesmake use of high resolution overhead imagery data to determine how todistribute the population density of large, low resolution cells (e.g.,1000 m) of a particular geographic region among a plurality of smaller,high resolution cells (e.g., 100 m) within the larger cells.

Broadly, one aspect disclosed herein includes identifying areas ofbuilt-up structures in at least one input overhead image of a geographicregion, obtaining population density estimates of a plurality of firstportions (e.g., larger, low resolution cells) of the geographic region,and using information related to the areas of built-up structures in theat least one input overhead image within the first portions to allocatethe population density estimates across a plurality of second portions(e.g., smaller, high resolution cells) of the geographic region.

For example, a low abstraction information layer, in which imagedescriptors corresponding to the built-up extent are organized intohomogeneous regions, may be extracted from any appropriate highresolution (e.g., HR, VHR, etc.) overhead image data (e.g.,multispectral, panchromatic, red-green-blue (RGB), etc.) of theparticular geographic area (e.g., city, region, etc.). In onearrangement, the low abstraction information layer may be in the form ofa plurality of multi-scale morphological image descriptors for each of arespective plurality of pixels of at least one input overhead image ofthe geographic area or region (e.g., at a spatial resolution of 2 m orthe like). For instance, the low abstraction information layer may be inthe form of a non-linear mixture model such as theCharacteristic-Saliency-Level or CSL model which may be obtained from aDifferential Area Profile (DAP) vector field (VF) generated from theoverhead image data.

The step of using the areas of built-up structures in the at least oneinput overhead image within each of the first portions (the larger, lowresolution cells) to allocate the population density estimates across aplurality of second portions (the smaller, high resolution cells) withinthe first portions may include obtaining the plurality of multi-scalemorphological image descriptors for each of a respective plurality ofpixels of the areas of built-up structures in the at least one inputoverhead image of the geographic region, training a linear model withthe plurality of population density estimates of the plurality of firstportions and the multi-scale morphological image descriptors within eachof the plurality of first portions to obtain a plurality of respectiveweighting factors (e.g., collectively, a weighting vector), and usingthe weighting factors to allocate the population density estimate of thefirst portions across the plurality of second portions.

For instance, the training step may include decomposing the multi-scalemorphological image descriptors within each of the first portions into arespective plurality of first vectors that each include a plurality ofentries (e.g., where each entry includes a value that represents afrequency of one of a plurality of fixed values of the multi-scalemorphological image descriptors within a respective one of the firstportions) and combining the plurality of first vectors into a matrix.The training step may also include generating a second vector thatincludes a plurality of entries (e.g., where each entry includes a valuethat represents one of the population density estimates of a respectiveone of the plurality of first portions of the geographic region). Thelinear model may represent a difference between the second vector and aproduct of the matrix and a third vector that includes a plurality ofentries. The training step may thus include determining a particularplurality of values of the respective plurality of entries of the thirdvector that minimizes the difference, where the particular plurality ofvalues is the plurality of weighting factors.

Once the weighting factors have been obtained, the method may utilizethe weighting factors to determine population density estimates for theplurality of second portions (e.g., the smaller, high resolution cells)of the geographic area. In this regard, the method may include, for eachsecond portion, decomposing the multi-scale morphological imagedescriptors within the second portion into a fourth vector that includesa plurality of entries (e.g., where each entry includes a value thatrepresents a frequency of one of the plurality of fixed values of themulti-scale morphological image descriptors within the second portion),and manipulating the fourth vector with the weighting vector to obtainthe population density estimate for the second portion. For instance,the manipulating step may include obtaining an inner product of thefourth vector and the weighting vector, and multiplying the innerproduct by a total quantity of pixels in the second portion of the atleast one overhead image to obtain the population density estimate forthe second portion. The plurality of population density estimates of therespective plurality of second portions may be mapped into a resultantimage of the geographic region.

In one arrangement, population density estimates in the small, highresolution cells may be reallocated and/or otherwise constrained so thatthe geographic scope of the estimates more closely correspond with thoseof the larger, low resolution cells. As an example, it may be the casethat a geographic scope of the relatively higher population densityportions in the higher resolution cells is greater (e.g., extendsfarther outward) than that in the corresponding lower resolution cells.For instance, the method may include using the population densityestimate of the first portion to constrain the population densityestimates of the second portions of the first portion, such as bynormalizing the population density estimates of the second portions intoa plurality of normalized population density estimates, and multiplyingthe population density estimate of the first portion by the normalizedpopulation density estimates to obtain corrected population densityestimates for the second portions.

In another aspect, a system for generating high resolution populationdensity estimation cells of a geographic region from low resolutionpopulation density estimation cells and high resolution overhead imageryof the geographic region includes a training engine, executable by aprocessor, that trains a linear model to obtain a plurality of weightingfactors to be used for determining population density estimates of aplurality of high resolution cells within a plurality of low resolutioncells of a geographic area. The linear model includes a) a respectiveplurality of population density estimates of the plurality of lowresolution cells and b) a plurality of multi-scale morphological imagedescriptors of a respective plurality of pixels of at least one inputoverhead image of the geographic region within each of the plurality oflow resolution cells. The system also includes an estimation engine,executable by the processor, that estimates a plurality of populationdensity estimates of the respective plurality of high resolution cellswithin each of the low resolution cells of the geographic region usingthe plurality of weighting factors and a plurality of multi-scalemorphological image descriptors within each of the plurality of highresolution cells.

Any of the embodiments, arrangements, or the like discussed herein maybe used (either alone or in combination with other embodiments,arrangement, or the like) with any of the disclosed aspects. Merelyintroducing a feature in accordance with commonly accepted antecedentbasis practice does not limit the corresponding feature to the singular.Any failure to use phrases such as “at least one” does not limit thecorresponding feature to the singular. Use of the phrase “at leastgenerally,” “at least partially,” “substantially” or the like inrelation to a particular feature encompasses the correspondingcharacteristic and insubstantial variations thereof. Furthermore, areference of a feature in conjunction with the phrase “in oneembodiment” does not limit the use of the feature to a singleembodiment.

In addition to the exemplary aspects and embodiments described above,further aspects and embodiments will become apparent by reference to thedrawings and by study of the following descriptions.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified block diagram illustrating a process ofgenerating high resolution population density estimation data from highresolution overhead imagery and low resolution population densityestimation data.

FIG. 2 is a block diagram of an automated high resolution populationdensity estimation system according to one embodiment.

FIG. 3 is a flow diagram of a method for generating high resolutionpopulation density estimation data from high resolution overhead imageryand low resolution population density estimation data.

FIG. 4a graphically illustrates multi-scale morphological imagedescriptors generated from multispectral imagery acquired by theWorldView 2 satellite over a portion of the city of Kabul, Afghanistanin 2012.

FIG. 4b illustrates low resolution population density estimation cellsover the portion of the city of Kabul, Afghanistan in 2012 of FIG. 4 a.

FIG. 5a illustrates multispectral imagery acquired by the WorldView 2satellite over a portion of the city of Kabul, Afghanistan in 2012.

FIG. 5b illustrates multi-scale morphological image descriptorsgenerated from the multispectral imagery of FIG. 5 a.

FIG. 5c is a mask for use in automatically removing the imagedescriptors of non-built-up portions from FIG. 5 b.

FIG. 6a presents an image of a portion of Kabul, Afghanistan thatgraphically depicts low resolution population density estimation cells.

FIG. 6b presents another image of the same portion of Kabul, Afghanistanas in FIGS. 6b , but that graphically depicts high resolution populationdensity estimation cells generated using the automated high resolutionpopulation density estimation system of FIG. 2.

FIG. 6c presents another image similar to that in FIG. 6b , but where ageographic scope of the high resolution population density estimatecells has been constrained using the geographic scope of correspondinglow resolution population density estimation cells of FIG. 6 a.

DETAILED DESCRIPTION

Disclosed herein are utilities (e.g., systems, processes, etc.) forefficiently and automatically generating high resolution populationdensity estimate data sets of a geographic area using known lowresolution population density estimate data sets of the geographic areaand high resolution overhead imagery of the geographic area. Initially,a linear model between population density estimates of a plurality oflow resolution cells (e.g., 1000 m) over the geographic area and amatrix of histograms (e.g., vectors) of image descriptors (e.g.,multi-scale morphological image descriptors) within each of the lowresolution cells may be trained to obtain a plurality of weightingfactors or values. Histograms (e.g., vectors) of image descriptorsascertained in each of a plurality of high resolution cells (e.g., 100m) over the geographic area may then be appropriately manipulated withthe weighting vector to obtain population density estimates for each ofthe high resolution cells.

At the outset, it is noted that, when referring to the earth herein,reference is made to any celestial body of which it may be desirable toacquire images or other remote sensing information. Furthermore, whenreferring to “overhead” imagery herein, such imagery may be obtained byany spacecraft, satellite, aircraft, and/or the like capable ofacquiring images or other remote sensing information. Furthermore, theutilities described herein may also be applied to other imaging systems,including imaging systems located on the earth or in space that acquireimages of other celestial bodies. It is also noted that the drawingfigures contained herein are not necessarily drawn to scale and thatsuch figures have been provided for the purposes of discussion andillustration only.

Generally, high resolution images of selected portions of a celestialbody's surface have become a product desired and used by governmentagencies, corporations, and individuals. For instance, many consumerproducts in common use today include images of the Earth's surface, suchas Google® Earth. Various types of remote sensing image collectionplatforms may be employed, including aircraft, earth-orbitingsatellites, and the like. In the case of a consumer digital camera, asone non-limiting example, an image sensor is generally arranged in anarea array (e.g., 3,000 rows of 3,000 pixels each, or 9,000,000 totalpixels) which collects the image area in a single “snapshot.” In thecase of satellite-based imaging, as another non-limiting example, the“push-broom scanning” principle is sometimes employed whereby each imagesensor includes a relatively small number of rows (e.g., a couple) of agreat number of pixels (e.g., 50,000 or more) in each row. Each row ofpixels may be scanned across the earth to build an image line by line,and the width of the image is the product of the number of pixels in therow times the pixel size or resolution (e.g., 50,000 pixels at 0.5 meterground resolution produces an image that is 25,000 meters wide). Thelength of the image is controlled by the scan duration (i.e. number oflines), which is typically settable for each image collected. Theresolution of overhead images varies depending on factors such as theparticular instrumentation utilized, the altitude of the satellite's (orother aircraft's) orbit, and the like.

Image collection platforms (e.g., aircraft, earth-orbiting satellites,etc.) may collect or acquire various types of imagery in one or moremanners. As one non-limiting example, image collection platforms mayperform panchromatic collection of scenes of a celestial body whichgenerally refers to the collection of image data across a single broadrange of wavelengths (e.g., all visible light, from near infrared (NIR)to near ultraviolet (NUV), etc.). As another non-limiting example, imagecollection platforms may additionally or alternatively capture imagedata within the visible light band and include respective filters toseparate the incoming light into red, green and blue portions. As afurther non-limiting example, image collections platforms mayadditionally or alternatively perform multispectral collection of scenesof a celestial body which generally refers to the collection of imagedata at multiple specific spectral bands across the electromagneticspectrum (e.g., within bands both inside and outside of the visiblelight range such as NIR, short wave infrared (SWIR), far infrared (FIR),etc.). For instance, a satellite may have one image sensor that issensitive to electromagnetic radiation across only a first spectral band(e.g., the visible light band, such as a wavelength range of about380-750 nm) in addition to one or more additional image sensors that aresensitive to electromagnetic radiation only across other spectral bands(e.g., NIR, 750-1400 nm; SWIR, 1400-3000 nm; etc.). Multi-spectralimaging may allow for the extraction of additional information from theradiance received at a satellite after being reflected from the Earth'ssurface (which may include atmospheric effects such as from aerosols,clouds, etc.).

As discussed previously, there are generally few global data setsavailable that could be used to map the human settlements. In the caseof LandScan and GPW, for instance, available global population data setsinclude population density estimation cell sets at coarse resolutions of1000 m or the like (e.g., where each population density estimation cellis in the form of a square that represents 1000 m on a side). While suchpopulation density data sets may be somewhat useful in conveying generalpopulation density estimates over large areas, they are limited in theirability to provide more fine grained population density estimates. Inthe case of an urban area, for instance, population densities may varywidely just between adjacent street blocks. In this regard, it may beuseful to utilize high resolution overhead image data to obtainpopulation density estimation data sets at resolutions higher than thosecurrently available.

In this regard, FIG. 1 presents a simplified block diagram of a system100 that may be used to generate and map high resolution populationdensity estimation data sets of a geographic area or region using knownlow resolution population density estimation data sets of the geographicarea (e.g., LandScan population density data) in addition to anyappropriate pixel image descriptors (e.g., characteristic, saliency,brightness, pixel intensity, grayscale value, etc.) of built-up areas(e.g., houses, buildings, tents, and/or other man-made 3-dimensionalstructures) obtained from any appropriate HR/VHR overhead imagery (e.g.,<1-10 m spatial resolution overhead image data obtained by a number ofheterogeneous platforms such as SPOT 2 and 5, CBERS 2B, RapidEye 2 and4, IKONOS® 2, QuickBird 2, WorldView 1, 2, and/or 3). Initially, thesystem 100 obtains low resolution population density estimates 104(e.g., made up of a grid of 1000 m² population density cells, etc.) overa particular geographic area (e.g., city, region, etc.) and anyappropriate image descriptors identifying built-up areas from HR/VHRoverhead imagery data 108 of the geographic area. The system 100 thenperforms the automated generation 112 of high resolution populationdensity estimates from the previously obtained data 104, 108 to obtainhigh resolution population density estimates 116 (e.g., made up of agrid of cells having areas smaller than those of the low resolutionpopulation density cells, such as 100 m² population density cells, 10 m²population density cells, etc.) over the geographic area.

FIG. 2 presents a block diagram of an automated high resolutionpopulation density estimation system 200 that may be used to implementthe automated generation 112 of high resolution population densityestimation data sets (e.g., made up of a plurality of high resolutioncells) shown in FIG. 1. Although depicted as a single device (e.g.,server, workstation, laptop, desktop, mobile device, and/or othercomputing device), one or more functionalities, processes or modules ofthe system 200 may be allocated or divided among a plurality ofmachines, devices and/or processes which may or may not be embodied in asingle housing. In one arrangement, functionalities of the server 200may be embodied in any appropriate cloud or distributed computingenvironment.

Broadly, the system 200 may include memory 204 (e.g., one or more RAM orother volatile memory modules, etc.), a processing engine or unit 208(e.g., one or more CPUs, processors, processor cores, or other similarpieces of hardware) for executing computer readable instructions fromthe memory 204, storage 212 (e.g., one or more magnetic disks or othernon-volatile memory modules or non-transitory computer-readablemediums), and/or a number of other components 216 (e.g., input devicessuch as a keyboard and mouse, output devices such as a display andspeakers, and the like), all of which may be appropriatelyinterconnected by one or more buses 220. While not shown, the system 200may include any appropriate number and arrangement of interfaces thatfacilitate interconnection between the one or more buses 220 and thevarious components of the system 200 as well as with other devices(e.g., network interfaces to allow for communication between the system200 and other devices over one or more networks, such as LANs, WANs, theInternet, etc.).

The system 200 may retrieve any appropriate low resolution populationdensity estimation data 224 of a particular geographic region (e.g.,LandScan population density kilometric cells) as well as any appropriateimage descriptors 228 of pixels corresponding to built-up in HR/VHRoverhead imagery of the geographic region (e.g., as well as the HR/VHRimagery itself) and store the same in any appropriate form in storage212 (e.g., such as in one or more databases and manageable by anyappropriate database management system (DBMS) to allow the definition,creation, querying, update, and administration of the databases). Theprocessing engine 208 may execute a DBMS or the like to retrieve andload the low resolution population density estimation data 224 and theimage descriptors 228 into the memory 204 for manipulation by a numberof engines or modules of the system 200 that are discussed in moredetail below.

As shown, the system 200 may include a “training” engine 232 that isbroadly configured to generate a weighting vector 236 of weightingvalues from the low resolution population density estimation data 224and the image descriptors 228, an “estimation” engine 233 that isbroadly configured to utilize the weighting vector 236 to estimatepopulation density estimates for high resolution cells 244 of thegeographic area, and a “mapping” engine 234 that is broadly configuredto map the high resolution population density estimation cells 244 intoone or more resultant images 252 of the geographic area.

Each of the engines (and/or other engines, modules, logic, etc.)disclosed and/or encompassed herein may be in the form of one or moresets of computer-readable instructions for execution by the processingunit 208 and that may be manipulated by users in any appropriate mannerto perform the automated generation of high resolution populationdensity estimation cells and presentation thereof on a display (notshown). In this regard, the combination of the processor 208, memory204, and/or storage 212 (i.e., machine/hardware components) on the onehand and the various engines/modules disclosed herein in one embodimentcreate a new machine that becomes a special purpose computer once it isprogrammed to perform particular functions of the high resolutionpopulation density estimation utilities disclosed herein (e.g., pursuantto instructions from program software). In one arrangement, anyappropriate portal in communication with the various engines may run onthe system 200 and be accessible by users (e.g., via any appropriatebrowser) to access the functionalities of the system 200. While thevarious engines have been depicted in FIG. 2 as being separate ordistinct modules, it is to be understood that the functionalities orinstructions of two or more of the engines may actually be integrated aspart of the same computer-readable instruction set and that the engineshave been depicted in the manner shown in FIG. 2 merely to highlightvarious functionalities of the system 200. Furthermore, while theengines have been illustrated as being resident within the (e.g.,volatile) memory 204 (e.g., for execution by the processing engine 208),it is to be understood that the engines may be stored in (e.g.,non-volatile) storage 212 (and/or other non-volatile storage incommunication with the system 200) and loaded into the memory 204 asappropriate.

To facilitate the reader's understanding of the various engines of thesystem 200, additional reference is now made to FIG. 3 which illustratesa method 300 for use in performing the high resolution populationdensity estimation utilities disclosed herein. While specific steps (andorders of steps) of the method 300 have been illustrated and will bediscussed, other methods (including more, fewer or different steps thanthose illustrated) consistent with the teachings presented herein arealso envisioned and encompassed within the present disclosure.

The method 300 may begin by obtaining 304 image descriptors (e.g.,multi-scale morphological image descriptors) of pixels of at least oneoverhead image of a geographic area or region that correspond withbuilt-up portions of the geographic area in addition to obtaining 308low resolution population density estimate data for the geographic area.In one arrangement, a low abstraction information layer in which imageelements corresponding to the built-up extent may be organized intohomogeneous regions may be extracted from HR/VHR overhead image data(e.g., multispectral, panchromatic, red-green-blue (RGB), etc.) of theparticular geographic area (e.g., city, region, etc.).

As just one example, the low abstraction information layer may be in theform of a non-linear mixture model such as theCharacteristic-Saliency-Level or CSL model which may be obtained from aDifferential Area Profile (DAP) vector field (VF) generated from theoverhead image data. For instance, the overhead image data (e.g., 2 mspatial resolution or the like) may be organized into a plurality ofhierarchically arranged, connected components of a hierarchical datastructure, such as a rooted, uni-directed tree with its leavescorresponding to a regional maxima of the input image(s) and its rootcorresponding to a single connected component defining the background ofthe input image(s). At least one morphological attribute filter (e.g.,an edge preserving operator) may then progressively accept (or reject)connected components of the tree based on some attribute criterion. Forinstance, attribute openings and attribute closings may be used wherebythe intensity value (e.g., grayscale) of each connected component isassessed at each of a number of progressively increasing and decreasingpredetermined intensity levels and the component is rejected if theintensity value is not higher or lower than each respectiveprogressively increasing or decreasing predetermined intensity level.For each pixel in the image, positive and negative response vectors maybe generated each having a number of entries respectively including theintensity difference (e.g., contrast) between connected componentswithin which the pixel is disposed at each respective predeterminedintensity level. The positive and negative response vectors may beconcatenated into a DAP (e.g., non-linear spatial signature) for eachpixel and the DAPs of all pixels may form the DAP VF.

The largest entry in the DAP for each pixel may be a “saliency” (S)multi-scale morphological image descriptor which may encode the areas ofbright features (e.g., linked to built-up on a statistical basis) anddark features (e.g., linked to shadow sizes which provide an indicationof built-up height) which collectively contain information aboutbuilt-up volume. The minimum scale at which the largest entry occurredmay be a “characteristic” (C) multi-scale morphological imagedescriptor. The highest peak component level from which S was determinedmay be a “level” (L) multi-scale morphological image descriptor. In onearrangement, the extracted built-up extent may be used as a mask toretain only those portions of the low or medium abstraction information(e.g., semantic) layer corresponding to the built-up.

FIG. 4a presents a graphical representation (e.g., as may be presentedon any appropriate display or monitor in communication with the system200 of FIG. 2) of multi-scale morphological image descriptors (e.g., afusion of the C and S descriptors discussed previously) of pixels ofbuilt-up areas generated from multispectral imagery acquired by theWorldView 2 satellite over a portion of Kabul, Afghanistan (where theblack areas devoid of image descriptors depict non-built-up portions ofKabul). In this figure, a Hue-Saturation-Value (HSV) transform/logic hasbeen used to produce a color output whereby hue (e.g., color and/orshade) has been mapped to the S layer, saturation is constant, and valuehas been mapped to the C layer. For instance, the particular hue (e.g.,which corresponds to the S layer) of a pixel may convey the area of abuilt-up structure (e.g., between 100 m²-300 m², between 300 m²-600 m²,etc.), the particular value (e.g., which corresponds to the C layer) ofa pixel may convey the confidence in the built-up structure (e.g., wherea higher contrast may indicate a greater confidence about a structurecorresponding to either a building or a shadow), and/or the like.

FIG. 4b illustrates low resolution population density estimation cells400 (e.g., 1000 m² cells) corresponding to the portion of Kabul,Afghanistan of FIG. 4a . For instance, a first low resolution cell 404of FIG. 4b may be used to isolate and extract the CS values (e.g.,and/or other morphological image descriptors) from FIG. 4a within thegeographic scope of the first low resolution cell 404 for reasons thatwill be discussed in more detail below. Other ones of the low resolutioncells 400 may be similarly used to isolate CS values corresponding tosuch other low resolution cells 400. In this example, each of the lowresolution cells 400 has been differently shaded to convey a differentpopulation density estimate for each of the cells (e.g., where the firstshade of first low resolution cell 404 indicates a population densityestimate of 500, the second shade of second low resolution cell 408indicates a population density estimate of 250, and/or the like). Ofcourse, other manners of depicting population density estimates of thelow resolution cells 400 are also envisioned and encompassed herein(e.g., color, saturation, etc.).

As discussed above, the pixel image descriptors (e.g., multi-scalemorphological image descriptors) obtained at 304 in the method 300 arethose that represent built-up areas of the geographic area (e.g., asopposed to those not representing built-up areas of the geographic area,such as those areas illustrated in all black in FIG. 4a ). In onearrangement, the built-up extent and/or other structures of interest(e.g., buildings, houses, shelters, tents, etc.) may be extracted fromor otherwise identified in any appropriate manner from HR/VHR overheadimage data (e.g., multispectral, panchromatic, red-green-blue (RGB),etc.) of the geographic area (e.g., images different than or the same asthat/those from which the CS values and/or other pixel image descriptorswere extracted) by way of the classification of texture parameters inthe overhead image data and used (e.g., as a mask) to only isolate thoseCS values and/or other pixel image descriptors corresponding to thebuilt-up extent. For instance, one manner of extracting built-up fromhigh resolution overhead imagery is disclosed in U.S. patent applicationSer. No. 13/955,268 and entitled “Automatic Generation of Built-upLayers from High Resolution Satellite Image Data,” which has beenincorporated herein by reference in its entirety. Another manner ofextracting built-up from high resolution overhead imagery is disclosedin U.S. patent application Ser. No. 14/013,904 and entitled “AutomaticExtraction of Built-Up Footprints from High Resolution Overhead Imagerythrough Manipulation of Alpha-Tree Data Structures,” which has beenincorporated herein by reference in its entirety. However, other mannersof extracting built-up are encompassed within the scope of the presentdisclosure.

In one arrangement, the CS and/or other image descriptor values may beextracted from HR/VHR overhead imagery (e.g., one or more input images)at a first spatial resolution (e.g., 2 m) while the built-up extent foruse in isolating the CS and/or other image descriptor valuescorresponding to the built-up extent may be extracted from HR/VHRoverhead imagery (e.g., one or more input images) at a second spatialresolution (e.g., 10 m) that is less than the first spatial resolution(e.g., 2 m). As used here, one spatial resolution being “less than”another spatial resolution means that the one spatial resolution is of alarger numerical value than the other spatial resolution, and viceversa. For instance, FIG. 5a illustrates multispectral imagery acquiredby the WorldView 2 satellite over a portion of Kabul, Afghanistan, FIG.5b illustrates multi-scale morphological image descriptors (e.g., a CSlayer) of the built-up extent of the portion of Kabul, Afghanistangenerated from the multispectral imagery of FIG. 5a , and FIG. 5c is amask for use in automatically removing those portions of the CS layer inFIG. 5b corresponding to non-built-up portions.

Once the image descriptors of the built-up extent have been obtained 304and the population density estimates of the low resolution cells havebeen obtained 308 for the particular geographic area, the method 300 mayproceed to training 312 a linear model to obtain a weighting vector tobe used for estimating population densities for a plurality of smaller,high resolution cells over the geographic region. For instance, thetraining engine 232 of the system 200 (see FIG. 2) may obtain aplurality of image descriptors 228 of the built-up extent of aparticular geographic area (e.g., the CS values represented by the pinkand green colors in FIG. 4a ) as well as population density data 224 fora plurality of low resolution cells over the geographic area (e.g.,those in FIG. 4b ). Thereafter, the training engine 232 may decompose316 the image descriptors corresponding to built-up within each of thelow resolution cells into a respective plurality of first histograms orvectors 236, where each vector generally conveys the frequency of eachof a plurality of image descriptor values within the respective lowresolution cell.

For example, each of the various values of the image descriptors of thevarious pixels within the low resolution cells (e.g., where each pixelassociated with built-up includes a value representing a fusion of itsrespective C and S values of FIG. 4a ) may be initially resolved intoone of a number of fixed and/or normalized values, such as one of 255values (e.g., the maximum number representable by an eight-bit number)and/or the like. For example, if the range of the CS fusion valueswithin the low resolution cells in the geographic area or region spannedfrom 0.1 to 10, then a fixed value 1 could be associated with CS fusionvalue 0, a fixed value 2 could be associated with CS fusion value 0.03,a fixed value 3 could be associated with CS fusion value 0.06, and soon. For each pixel, the training engine 232 may thus obtain itsrespective CS fusion value (or other image descriptor value), identify aclosest CS fusion value having a respective fixed value, and assign thefixed value to the pixel.

Thereafter, a histogram including the frequencies of each of the fixedvalues within each of the low resolution cells may be expressed in theform of the following vector for each low resolution cell:x _(i) =[a ₁(b ₁), a ₂(b ₂), . . . a _(n)(b _(n))],

where each of the entries a₁(b₁), a₂(b₂), etc. corresponds to thefrequency of a particular one of a plurality of fixed values b₁, b₂,etc. among all the pixels of the built-up in the particular lowresolution cell.

Assuming for purposes of simplicity that the pixel image descriptorswere resolved into three fixed values 1, 2 and 3, a vector x₁=[0.20,0.45, 0.35] would indicate that 20% of the built-up pixels in a firstlow resolution cell (e.g., first low resolution cell 404 in FIG. 4a )had one or more image descriptors resolved into fixed value 1, 45% ofthe built-up pixels in a first low resolution cell had one or more imagedescriptors resolved into fixed value 2, and 35% of the built-up pixelsin a first low resolution cell had one or more image descriptorsresolved into fixed value 3. First vectors may be similarly determinedfor each of the other low resolution cells of the particular geographicarea.

The training engine 232 may then appropriately combine 320 the pluralityof first vectors 236 (e.g., x₁, x₂, x₃, etc.) respectively correspondingto each of the plurality of low resolution cells (e.g., low resolutioncells 400 in FIGS. 4a-4b ) into a matrix 240 (e.g., matrix “x”) asfollows:

${x = \begin{bmatrix}{a_{1}\left( {b_{1}\left( x_{1} \right)} \right)} & {a_{2}\left( {b_{2}\left( x_{1} \right)} \right)} & \ldots & {a_{n}\left( \;{b_{n}\left( x_{1} \right)} \right)} \\{a_{1}\left( {b_{1}\left( x_{2} \right)} \right)} & {a_{2}\left( {b_{2}\left( x_{2} \right)} \right)} & \cdots & {a_{n\;}\left( {b_{n}\left( x_{2} \right)} \right)} \\{a_{1}\left( {b_{1}\left( x_{3} \right)} \right)} & {a_{2}\left( {b_{2}\left( x_{3} \right)} \right)} & \cdots & {a_{n}\left( {b_{n}\left( x_{3} \right)} \right)}\end{bmatrix}},$

where each row of the matrix x includes the first vector of a differentrespective low resolution cell of the geographic region.

As shown in FIGS. 2 and 3, the training engine 232 may also obtain orgenerate 324 a second vector 244 (e.g., vector “y”) including aplurality of entries, where each entry includes the known populationdensity estimate of a respective one of the plurality of low resolutioncells as follows:y=[y₁, y₂ . . . y_(n)],

and where each of the entries y₁, y₂, etc. corresponds to the populationdensity estimate of a particular one of the low resolution cells (e.g.,as obtained from LandScan data or the like).

The training engine 232 may then generate a linear model 248 thatincludes the matrix 240 (e.g., matrix 4 the second vector 244 (e.g.,vector y), and a third vector 252 for purposes of identifying aparticular plurality of weighting factors (collectively, a weightingvector 256) that may be used to estimate population densities for aplurality of smaller high resolution cells over the geographic area. SeeFIG. 2. More specifically, and with reference to FIGS. 2 and 3, themethod 300 may include identifying 328 a plurality of values of thethird vector 252 that minimizes a difference between a) the secondvector 244 and b) a product of the matrix 240 and the third vector 252,and then equating 332 the particular plurality of values of the thirdvector 252 that minimizes the above difference to the weighting vector256 of weighting factors. For instance, the third vector 252 may beexpressed as follows:w=[w ₁(b ₁), . . . w ₂(b₂), . . . w _(n)(b _(n))],

where each of the entries w₁(b₁), w₂(b₂), etc. corresponds to a yet tobe determined weighting value or factor corresponding to a respectiveone of the fixed values b₁, b₂, etc.

In this regard, the linear model 248 may be expressed as follows:

${{\begin{bmatrix}{w_{1}\left( b_{1} \right)} \\{w_{2}\left( b_{2} \right)} \\\vdots \\{w_{n}\left( b_{n} \right)}\end{bmatrix} \times \begin{bmatrix}{a_{1}\left( {b_{1}\left( x_{1} \right)} \right)} & {a_{2}\left( {b_{2}\left( x_{1} \right)} \right)} & \ldots & {a_{n}\left( \;{b_{n}\left( x_{1} \right)} \right)} \\{a_{1}\left( {b_{1}\left( x_{2} \right)} \right)} & {a_{2}\left( {b_{2}\left( x_{2} \right)} \right)} & \cdots & {a_{n\;}\left( {b_{n}\left( x_{2} \right)} \right)} \\{a_{1}\left( {b_{1}\left( x_{3} \right)} \right)} & {a_{2}\left( {b_{2}\left( x_{3} \right)} \right)} & \cdots & {a_{n}\left( {b_{n}\left( x_{3} \right)} \right)}\end{bmatrix}} = {\left. \begin{bmatrix}y_{1} \\y_{2} \\\vdots \\y_{n}\end{bmatrix} \right.\sim 0}},$

Stated differently, multiplication of the vector w (e.g., third vector252) and the matrix x (e.g., matrix 240) results in a vector “z” (notshown) that, when the vector y (e.g., second vector 244) is subtractedfrom vector z, results in a number substantially as close to zero aspossible. In the above linear model 248, all the values of the entriesin the matrix x and vector y are previously determined and/or otherwiseobtained as discussed previously. In this regard, the training engine232 seeks to optimize the linear model 248 by determining the particularplurality of values of the entries w₁(b₁), w₂(b₂), etc. of the vector wthat limits the difference between the vector z and the vector y. Thevalues of the vector w may be constrained to be positive during theidentifying step 328 (e.g., to substantially ensure that each weightingfactor connotes an average number of people leaving in a pixelcorresponding to a certain CS value). It is noted that the term “thirdvector” is used herein connote a vector of entries whose values are asyet unknown while the term “weighting vector” is used herein to connotea vector including the specific entries of the third vector aftertraining and optimization of the linear model 248 by the training engine232 in the manner disclosed herein.

Referring to FIG. 3, the method 300 may then include estimating 336population density estimates of a plurality of high resolution cellswithin the geographic region (e.g., such as that of a high resolutioncell 412 in FIGS. 4a-4b having an area smaller than that of the firstlow resolution cell 404). More particularly, the estimation engine 234of the system 200 may obtain the pixel image descriptors 228 anddecompose 340 the image descriptors corresponding to built-up withineach of the high resolution cells into a respective plurality of fourthvectors 260. For instance, each of the various pixel image descriptorvalues within the low resolution cells (e.g., the CS values within thehigh resolution cells 412 in FIGS. 4a-4b ) may be initially resolvedinto one of a number of fixed values (e.g., as discussed previously inrelation to the first vectors 236) and then a histogram including thefrequencies of each of the fixed values within each of the highresolution cells 412 may be expressed as a fourth vector 260 (e.g.,vector “d”) for each high resolution cell 460 as follows:d _(i) =[e ₁(b ₁), e ₂(b ₂), . . . e _(n)(b _(n))],

where each of the entries e₁(b₁), e₂(b₂), etc. corresponds to thefrequency of a particular one of a plurality of fixed values b₁, b₂,etc. among all the pixels of the built-up in the particular highresolution cell.

For each high resolution cell 412, the estimation engine 233 may thenmanipulate 344 each respective fourth vector 260 (e.g., the vector a)with the weighting vector 256 to obtain a population density estimatefor the high resolution cell 412. Generally, particular levels ofcertain ones of the pixel image descriptors are typically associatedwith greater numbers or magnitudes of people than are other ones of thefixed values. For instance, a first pixel having a first imagedescriptor value (e.g., a first CS fusion value) suggesting that thefirst pixel identifies a portion of a house may statistically identifyor imply a greater number or magnitude of people than does a secondpixel having a second image descriptor value (e.g., a second CS fusionvalue) suggesting that the second pixel identifies a portion of abridge. Furthermore, and as discussed herein, each of the imagedescriptor values of the pixels in the cells is resolved into one of anumber of fixed values b₁, b₂, etc. Accordingly, each weighting factor(e.g., w₁(b₁), w₂(b₂), etc.) of the weighting vector 256 generallyconnotes a particular magnitude of people to afford to or correlate witheach of the fixed values (e.g., b₁, b₂, etc.).

Thus, by multiplying the each entry e₁(b₁), e₂(b₂), etc. of a particularfourth vector 260 by its respective weighting factor w₁(b₁), w₂(b₂),etc., summing the results, and multiplying the sum by the total quantityof pixels in the particular high resolution cell, a population densityestimate for the high resolution cell may be obtained. Stateddifferently, the estimation engine 233 may obtain 348 an inner product(e.g., dot product) of the fourth vector 260 and the weighting vector256 and then multiply 352 the inner product by the total quantity ofpixels in the particular high resolution cell to obtain a populationdensity estimate 264 for the high resolution cell. This process may berepeated with additional high resolution cells of the geographic area toobtain a grid of high resolution cells over the geographic area.

EXAMPLE

An extremely simplistic vector d (fourth vector 260) of a highresolution cell 412 in FIG. 4b and weighting vector are the following:d=[0.2, 0.7, 0.1]w=[6, 2, 12]

Obtaining the inner product of the vectors dand wresults in thefollowing sum:[(0.2)×(6)]+[(0.7)×(2)]+[(0.1)×(12)]=[1.2+1.4+1.2]=3.8

Multiplying the sum by the total quantity of pixels in the highresolution cell in the overhead image(s) from which the imagedescriptors were obtained results in a population density estimate forthe particular high resolution cell. For instance, in the event that theoverhead image(s) from which the image descriptors were obtained had aspatial resolution of 2 m and the area of the high resolution cell was1000 m² (100 m×100 m), the total number of pixels in the high resolutioncell would be 1000/2 or 500. Thus, the population density estimate forthe high resolution cell in this example is 500×3.8 or 1900 people. Asimilar process may be performed to obtain population density estimatesfor other high resolution cells of the geographic region.

With reference again to FIGS. 2 and 3, the method 300 may includemapping 356 the population density estimates of the high resolutioncells of the geographic region (e.g., high resolution cells 412 of FIGS.4a-4b , only one being shown) into one or more resultant images 268which may be appropriately stored in storage 212. For instance, FIG. 6bpresents a resultant image of a portion of Kabul, Afghanistan thatincludes a plurality of high resolution population density estimationcells (e.g., 100 m cells) generated using the utilities disclosedherein. Each high resolution cell may be appropriately colored, shaded,etc. to connote the particular population density or population densityrange represented by the corresponding high resolution cell. In FIG. 6b, it can be seen how the high resolution cells closest to the center ofthe image are more brightly shaded indicating that greater populationdensities are found in the center of the image. For reference, FIG. 6apresents another image of the portion of Kabul, Afghanistan from FIG. 6bbut with low resolution population density estimation cells (e.g., 1000m cells), such as those obtained from LandScan or the like. It can beseen how the high resolution cells in FIG. 6b generated using theutilities disclosed herein advantageously provide analysts and the likewith more fine-grained population density estimates of a desiredgeographic region.

In some situations, the geographic scope of the population densityestimates of the high resolution cells of a particular geographic areamay exceed that of the population density estimates of the lowresolution cells. For instance, it can be seen how the relativelylighter portions in FIGS. 6b (e.g., indicating higher populationdensities) expand geographically farther out than do the lighterportions in FIG. 6a . Thus, it may be beneficial to reallocate orotherwise constrain such portions of the high resolution cells to moreclosely correspond with the geographic scope of the corresponding lowresolution cells.

In one arrangement, a Dasymetric reallocation process may be performedto more realistically place the high resolution cells over appropriateportions of the geographic region or area. For instance, for eachparticular low resolution cell of a geographic area (e.g., first lowresolution cell 404 of FIG. 4b ), the population density estimates ofeach of the high resolution cells (e.g., high resolution cells 412, onlyone being shown) within the low resolution cell may be obtained (e.g.,e₁, e₂, . . . e_(n)) and then normalized in any appropriate manner intonormalized values (e.g., ee₁, ee₂, . . . ee_(n), where Σee₁, ee₂, . . .ee_(n)=1). The original population density estimate of the larger, lowresolution cell may then be multiplied by each of the various normalizedvalues to obtain Dasymetric population density estimates for each of thesmaller, high resolution cells that substantially limit the geographicscope of the population density estimates of the smaller cells to thatof the larger cell. For instance, FIG. 6c presents another image similarto that in FIG. 6b , but where the high resolution cells areappropriately colored, shaded, etc. to depict their respectiveDasymetric population density estimates for purposes of generallyconstraining the geographic scope of the high resolution cells to thatof the corresponding low resolution cells within which the highresolution cells are located.

It will be readily appreciated that many additions and/or deviations maybe made from the specific embodiments disclosed in the specificationwithout departing from the spirit and scope of the invention. Forinstance, while the utilities disclosed herein have been discussed inthe context of population density estimates, it is to be understood thatthe teachings may be applied to obtain finer grain, high resolutiondensity estimates of other metrics such as population wealth metrics,building count, and/or the like.

Any of the embodiments, arrangements, or the like discussed herein maybe used (either alone or in combination with other embodiments,arrangement, or the like) with any of the disclosed aspects. Merelyintroducing a feature in accordance with commonly accepted antecedentbasis practice does not limit the corresponding feature to the singular.Any failure to use phrases such as “at least one” does not limit thecorresponding feature to the singular. Use of the phrase “at leastgenerally,” “at least partially,” “substantially” or the like inrelation to a particular feature encompasses the correspondingcharacteristic and insubstantial variations thereof. Furthermore, areference of a feature in conjunction with the phrase “in oneembodiment” does not limit the use of the feature to a singleembodiment. Still further, any use of “first,” “second,” “third,” etc.herein does not necessarily connote any specific order or arrangement ofcomponents and/or processes disclosed herein and has merely be used tofacilitate understanding of the teachings presented herein.

The utilities and related embodiments disclosed herein can beimplemented as one or more computer program products, i.e., one or moremodules of computer program instructions encoded on a computer-readablemedium for execution by, or to control the operation of, data processingapparatus. The computer-readable medium can be a machine-readablestorage device, a machine-readable storage substrate, a non-volatilememory device, a composition of matter affecting a machine-readablepropagated signal, or a combination of one or more of them. In thisregard, the disclosed utilities may encompass one or more apparatuses,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, or multiple processors or computers.In addition to hardware, the utilities may include code that creates anexecution environment for the computer program in question, e.g., codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, or a combination of one or moreof them.

A computer program (also known as a program, software, softwareapplication, script, or code) used to provide any of the functionalitiesdescribed herein (e.g., construction of the first and secondhierarchical data structures and the like) can be written in anyappropriate form of programming language including compiled orinterpreted languages, and it can be deployed in any form, including asa stand-alone program or as a module, component, subroutine, or otherunit suitable for use in a computing environment. A computer programdoes not necessarily correspond to a file in a file system. A programcan be stored in a portion of a file that holds other programs or data(e.g., one or more scripts stored in a markup language document), in asingle file dedicated to the program in question, or in multiplecoordinated files (e.g., files that store one or more modules,sub-programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers that are locatedat one site or distributed across multiple sites and interconnected by acommunication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit). Processors suitable for theexecution of a computer program may include, by way of example, bothgeneral and special purpose microprocessors, and any one or moreprocessors of any kind of digital computer. Generally, a processor willreceive instructions and data from a read-only memory or a random accessmemory or both. Generally, the elements of a computer are one or moreprocessors for performing instructions and one or more memory devicesfor storing instructions and data. The techniques described herein maybe implemented by a computer system configured to provide thefunctionality described.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of the disclosure or of what maybe claimed, but rather as descriptions of features specific toparticular embodiments of the disclosure. Furthermore, certain featuresthat are described in this specification in the context of separateembodiments can also be implemented in combination in a singleembodiment. Conversely, various features that are described in thecontext of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations, one or more features from a claimed combination can insome cases be excised from the combination, and the claimed combinationmay be directed to a subcombination or variation of a subcombination.

Similarly, while operations may be depicted in the drawings in aparticular order, this should not be understood as requiring that suchoperations be performed in the particular order shown or in sequentialorder, or that all illustrated operations be performed, to achievedesirable results. In certain circumstances, multitasking and/orparallel processing may be advantageous. Moreover, the separation ofvarious system components in the embodiments described above should notbe understood as requiring such separation in all embodiments, and itshould be understood that the described program components and systemscan generally be integrated together in a single software and/orhardware product or packaged into multiple software and/or hardwareproducts.

The above described embodiments including the preferred embodiment andthe best mode of the invention known to the inventor at the time offiling are given by illustrative examples only.

What is claimed is:
 1. A method for use in generating population densitydata sets of a geographic region from overhead imagery data, comprising:identifying areas of built-up structures in at least one input overheadimage of a geographic region; obtaining population density estimates ofa plurality of first portions of the geographic region; and with aprocessor, using the areas of built-up structures in the at least oneinput overhead image within the first portions to allocate thepopulation density estimates across a plurality of second portionswithin the first portions, wherein the using comprises: obtaining aplurality of multi-scale morphological image descriptors for each of arespective plurality of pixels of the areas of built-up structures inthe at least one input overhead image of the geographic region;training, with the processor, a linear model with a) the plurality ofpopulation density estimates of the plurality of first portions and b)the multi-scale morphological image descriptors within each of theplurality of first portions to obtain a plurality of respectiveweighting factors; and using the weighting factors to allocate thepopulation density estimate of each first portion across the pluralityof second portions of the first portions.
 2. The method of claim 1,wherein the training includes: decomposing the multi-scale morphologicalimage descriptors within each of the first portions into a respectiveplurality of first vectors that each include a plurality of entries,wherein each entry includes a value that represents a frequency of oneof a plurality of fixed values of the multi-scale morphological imagedescriptors within a respective one of the first portions; and combiningthe plurality of first vectors into a matrix.
 3. The method of claim 2,wherein the training includes: generating a second vector that includesa plurality of entries, wherein each entry includes a value thatrepresents one of the population density estimates of a respective oneof the plurality of first portions of the geographic region.
 4. Themethod of claim 3, wherein the linear model comprises a differencebetween a) the second vector and b) a product of the matrix and a thirdvector that includes a plurality of entries, and wherein the trainingincludes: determining a particular plurality of values of the respectiveplurality of entries of the third vector that minimizes the difference,wherein the particular plurality of values is the plurality of weightingfactors.
 5. The method of claim 4, wherein the plurality of weightingfactors are arranged in a weighting vector, and wherein the using theweighting factors to allocate the population density estimate of thefirst portions across the plurality of second portions includes, foreach second portion: decomposing the multi-scale morphological imagedescriptors within the second portion into a fourth vector that includesa plurality of entries, wherein each entry includes a value thatrepresents a frequency of one of the plurality of fixed values of themulti-scale morphological image descriptors within the second portion;obtaining an inner product of the fourth vector and the weightingvector; and multiplying the inner product by a total quantity of pixelsin the second portion of the at least one input overhead image to obtainthe population density estimate for the second portion.
 6. The method ofclaim 1, further comprising: mapping the plurality of population densityestimates of the respective plurality of second portions into aresultant image of the geographic region.
 7. The method of claim 1,further comprising for each first portion of the geographic region:using the population density estimate of the first portion to constrainthe population density estimates of the second portions of the firstportion.
 8. The method of claim 7, wherein the using the populationdensity estimate of the first portion to constrain the populationdensity estimates of the second portions of the first portion includes:normalizing the population density estimates of the second portions intoa plurality of normalized population density estimates; and multiplyingthe population density estimate of the first portion by the normalizedpopulation density estimates to obtain corrected population densityestimates for the second portions.
 9. The method of claim 1, wherein theat least one input overhead image comprises a spatial resolution of nogreater than 10 meters.
 10. The method of claim 9, wherein the at leastone input overhead image comprises a spatial resolution of no greaterthan 2 meters.
 11. The method of claim 1, wherein the first and secondportions are squares.
 12. A method for use in generating populationdensity data sets of a geographic region from overhead imagery data,comprising: identifying areas of interest in at least one input overheadimage of a geographic region; generating, with a processor for eachfirst portion of a plurality of first portions of the geographic region,a first vector of entries that respectively indicate the frequency ofeach of a plurality of different multi-scale image descriptors in theareas of interest in the first portion; combining the first vectors ofthe plurality of first portions into a matrix; generating, with theprocessor for each first portion, a second vector of entries thatrespective indicate population density estimates for the plurality offirst portions of the geographic region; determining, with theprocessor, a third vector of weighting values that respectively minimizea difference between a) the second vector and b) a product of the matrixand the vector of weighting values; and with the processor, using thethird vector of weighting values to allocate the population densityestimates of the plurality of first portions across a plurality ofsecond portions within each of the first portions.
 13. The method ofclaim 12, wherein the using comprises: generating, with the processorfor each second portion of the plurality of second portions, a fourthvector of entries that respectively indicate the frequency of each ofthe plurality of different multi-scale image descriptors in the areas ofinterest in the second portion; and manipulating the fourth vector ofentries with the third vector of weighting values to obtain populationdensity estimates for each second portion of the plurality of secondportions.
 14. The method of claim 13, wherein the manipulating includes:obtaining an inner product of the fourth vector and the third vector ofweighting values; and multiplying the inner product by a total quantityof pixels in the second portion of the at least one input overhead imageto obtain the population density estimate for the second portion. 15.The method of claim 12, further comprising: mapping the plurality ofpopulation density estimates of the respective plurality of secondportions into a resultant image of the geographic region.
 16. The methodof claim 12, further comprising for each first portion of the geographicregion: using the population density estimate of the first portion toconstrain the population density estimates of the second portions of thefirst portion.
 17. The method of claim 16, wherein the using thepopulation density estimate of the first portion to constrain thepopulation density estimates of the second portions of the first portionincludes: normalizing the population density estimates of the secondportions into a plurality of normalized population density estimates;and multiplying the population density estimate of the first portion bythe normalized population density estimates to obtain correctedpopulation density estimates for the second portions.
 18. The method ofclaim 12, wherein the areas of interest are built-up structures.